Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorith...Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficultto-program applications, and software applications. It is a collection of a variety of algorithms（e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.） that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore,nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.展开更多
为了减小室内环境因素对室内无线定位的影响,提高定位精度和扩大定位区域,提出了一种新的基于SVM的WiFi室内无线定位方法。通过将室内区域划分为多个小的区域,建立每个区域RSSI(received signal strength indication)和位置的非线性关系...为了减小室内环境因素对室内无线定位的影响,提高定位精度和扩大定位区域,提出了一种新的基于SVM的WiFi室内无线定位方法。通过将室内区域划分为多个小的区域,建立每个区域RSSI(received signal strength indication)和位置的非线性关系,再利用SVM强大的分类和回归能力来实现定位。实验结果表明,本定位方法能够达到1.83 m的定位精度,说明区域划分和非线性回归能够有效地控制误差范围和提高定位精度。展开更多
文摘Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficultto-program applications, and software applications. It is a collection of a variety of algorithms（e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.） that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore,nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.
文摘为了减小室内环境因素对室内无线定位的影响,提高定位精度和扩大定位区域,提出了一种新的基于SVM的WiFi室内无线定位方法。通过将室内区域划分为多个小的区域,建立每个区域RSSI(received signal strength indication)和位置的非线性关系,再利用SVM强大的分类和回归能力来实现定位。实验结果表明,本定位方法能够达到1.83 m的定位精度,说明区域划分和非线性回归能够有效地控制误差范围和提高定位精度。
